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/*! \file
  \brief

*/

#pragma once

#include "predicated_tile_iterator.h"
#include "cutlass/gemm/gemm.h"
#include "cutlass/layout/pitch_linear.h"

////////////////////////////////////////////////////////////////////////////////

namespace cutlass {
namespace epilogue {
namespace threadblock {

////////////////////////////////////////////////////////////////////////////////

/// Defines the optimal thread map for TensorOp accumulator layouts
template <typename ThreadblockShape_, typename WarpShape_, int PartitionsK,
          typename Element_, int ElementsPerAccess>
struct DefaultThreadMapTensorOp {
    using ThreadblockShape = ThreadblockShape_;
    using WarpShape = WarpShape_;
    static int const kPartitionsK = PartitionsK;
    using Element = Element_;
    static int const kElementsPerAccess = ElementsPerAccess;

    //
    // Definitions
    //

    struct Detail {
        /// Tensor Operations fundamentally perform operations on 8 rows
        static int const kTensorOpRows = 8;
        static int const kWarpSize = 32;

        static_assert(!(ThreadblockShape::kM % WarpShape::kM) &&
                              !(ThreadblockShape::kN % WarpShape::kN),
                      "Divisibility");

        /// Number of warps
        using WarpCount = gemm::GemmShape<ThreadblockShape::kM / WarpShape::kM,
                                          ThreadblockShape::kN / WarpShape::kN,
                                          kPartitionsK>;

        /// Number of participating threads
        static int const kThreads = WarpCount::kCount * kWarpSize;
    };

    //
    // ThreadMap
    //

    /// ThreadMap to be used by epilogue::PredicatedTileIterator satisfying
    /// concept OutputTileThreadMap
    using Type = OutputTileOptimalThreadMap<
            OutputTileShape<ThreadblockShape::kN, Detail::kTensorOpRows,
                            Detail::WarpCount::kM, 1, 1>,
            OutputTileShape<1, WarpShape::kM / Detail::kTensorOpRows, 1, 1,
                            WarpShape::kM / Detail::kTensorOpRows>,
            Detail::kThreads, kElementsPerAccess, sizeof_bits<Element>::value>;
};

////////////////////////////////////////////////////////////////////////////////

/// Defines the optimal thread map for TensorOp accumulator layouts
template <typename ThreadblockShape_, typename WarpShape_, int PartitionsK,
          typename Element_, int ElementsPerAccess, int InterleavedK>
struct DefaultInterleavedThreadMapTensorOp {
    using ThreadblockShape = ThreadblockShape_;
    using WarpShape = WarpShape_;
    static int const kPartitionsK = PartitionsK;
    using Element = Element_;
    static int const kElementsPerAccess = ElementsPerAccess;
    static int const kInterleavedK = InterleavedK;

    //
    // Definitions
    //

    struct Detail {
        /// Tensor Operations fundamentally perform operations on 8 rows
        static int const kTensorOpRows = 8;
        static int const kWarpSize = 32;

        static_assert(!(ThreadblockShape::kM % WarpShape::kM) &&
                              !(ThreadblockShape::kN % WarpShape::kN),
                      "Divisibility");

        /// Number of warps
        using WarpCount = gemm::GemmShape<ThreadblockShape::kM / WarpShape::kM,
                                          ThreadblockShape::kN / WarpShape::kN,
                                          kPartitionsK>;

        /// Number of participating threads
        static int const kThreads = WarpCount::kCount * kWarpSize;
    };

    //
    // ThreadMap
    //

    /// ThreadMap to be used by epilogue::PredicatedTileIterator satisfying
    /// concept InterleavedOutputTileThreadMap
    using Type = InterleavedOutputTileThreadMap<
            layout::PitchLinearShape<Detail::WarpCount::kM,
                                     Detail::WarpCount::kN>,
            layout::PitchLinearShape<WarpShape::kM / Detail::kTensorOpRows,
                                     WarpShape::kN / InterleavedK>,
            Detail::kThreads, kElementsPerAccess, sizeof_bits<Element>::value>;
};

////////////////////////////////////////////////////////////////////////////////

/// Defines the optimal thread map for TensorOp accumulator layouts
template <typename ThreadblockShape_, typename WarpShape_, int PartitionsK,
          typename Element_, int ElementsPerAccess, int InterleavedK>
struct DefaultInterleavedConvThreadMapTensorOp {
    using ThreadblockShape = ThreadblockShape_;
    using WarpShape = WarpShape_;
    static int const kPartitionsK = PartitionsK;
    using Element = Element_;
    static int const kElementsPerAccess = ElementsPerAccess;
    static int const kInterleavedK = InterleavedK;

    //
    // Definitions
    //

    struct Detail {
        /// Tensor Operations fundamentally perform operations on 8 rows
        static int const kTensorOpRows = 8;
        static int const kWarpSize = 32;

        static_assert(!(ThreadblockShape::kM % WarpShape::kM) &&
                              !(ThreadblockShape::kN % WarpShape::kN),
                      "Divisibility");

        /// Number of warps
        using WarpCount = gemm::GemmShape<ThreadblockShape::kM / WarpShape::kM,
                                          ThreadblockShape::kN / WarpShape::kN,
                                          kPartitionsK>;

        /// Number of participating threads
        static int const kThreads = WarpCount::kCount * kWarpSize;
    };

    //
    // ThreadMap
    //

    /// ThreadMap to be used by epilogue::MaskedTileIterator satisfying concept
    /// InterleavedOutputTileThreadMap
    using Type = InterleavedConvOutputTileThreadMap<
            MatrixShape<Detail::WarpCount::kM, Detail::WarpCount::kN>,
            MatrixShape<WarpShape::kM / Detail::kTensorOpRows,
                        WarpShape::kN / InterleavedK>,
            Detail::kThreads, kElementsPerAccess, sizeof_bits<Element>::value>;
};

////////////////////////////////////////////////////////////////////////////////

}  // namespace threadblock
}  // namespace epilogue
}  // namespace cutlass

////////////////////////////////////////////////////////////////////////////////
